Maria Littmann

发表

Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

B. Rost, N. Ben-Tal, Björn Schuller, 2020, Nature Machine Intelligence.

Contrastive learning on protein embeddings enlightens midnight zone

B. Rost, C. Orengo, I. Sillitoe, 2021, bioRxiv.

Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets

Kevin K. Yang, Amy X. Lu, B. Rost, 2021, Current protocols.

Clustering FunFams using sequence embeddings improves EC purity

B. Rost, C. Orengo, N. Bordin, 2021, bioRxiv.

PredictProtein - Predicting Protein Structure and Function for 29 Years

B. Rost, C. Sander, G. Vriend, 2021, Nucleic acids research.

Protein embeddings and deep learning predict binding residues for various ligand classes.

B. Rost, M. Heinzinger, Maria Littmann, 2021, Scientific Reports.

PredictProtein - Predicting Protein Structure and Function for 29 Years

B. Rost, C. Sander, G. Vriend, 2021, bioRxiv.

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Su Datt Lam, B. Rost, C. Orengo, 2022, bioRxiv.

LambdaPP: Fast and accessible protein-specific phenotype predictions

B. Rost, M. Heinzinger, Martin Steinegger, 2022, bioRxiv.

Refining Embedding-Based Binding Predictions by Leveraging AlphaFold2 Structures

Maria Littmann, Tobias Olenyi, K. Erckert, 2022, bioRxiv.

LambdaPP: Fast and accessible protein‐specific phenotype predictions

B. Rost, M. Heinzinger, Christian Dallago, 2022, bioRxiv.

Novel machine learning approaches revolutionize protein knowledge

C. Orengo, N. Bordin, C. Rauer, 2022, Trends in biochemical sciences.

FunFam protein families improve residue level molecular function prediction

B. Rost, C. Orengo, Maria Littmann, 2019, BMC Bioinformatics.

Protein embeddings and deep learning predict binding residues for various ligand classes

B. Rost, M. Heinzinger, Christian Dallago, 2021, Scientific Reports.

Embeddings from deep learning transfer GO annotations beyond homology

Burkhard Rost, Michael Heinzinger, Christian Dallago, 2020, Scientific Reports.

Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

N. Ben-Tal, Peter Hönigschmid, M. Niv, 2020, Nature Machine Intelligence.

CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models

B. Rost, C. Orengo, I. Sillitoe, 2022, bioRxiv.

Detailed prediction of protein sub-nuclear localization

Burkhard Rost, Mikael Bodén, Sebastian Seitz, 2019, BMC Bioinformatics.

FunFam protein families improve residue level molecular function prediction

Burkhard Rost, Christine Orengo, Maria Littmann, 2019, BMC Bioinformatics.

Supporting online material for: Clustering FunFams using sequence embeddings improves EC purity

B. Rost, C. Orengo, M. Heinzinger, 2021 .

CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

Silvio C. E. Tosatto, M. Y. Lobanov, Md. Wasi Ul Kabir, 2023, Nucleic Acids Res..

Supporting online material for: Protein embeddings and deep learning predict binding residues for various ligand classes

B. Rost, M. Heinzinger, Christian Dallago, 2021 .